The containment liner plate (CLP) is a thin layer of carbon steel material applied as a base for concrete structures protecting nuclear material. The structural health monitoring of the CLP is critical to ensure the safety of nuclear power plants. Hidden defects in the CLP can be identified utilizing ultrasonic tomographic imaging techniques such as the reconstruction algorithm for the probabilistic inspection of damage (RAPID) methodology. However, Lamb waves have a multimodal dispersion feature, which makes the selection of a single mode more difficult. Thus, sensitivity analysis was utilized since it allows for the determination of each mode's level of sensitivity as a function of frequency; the S0 mode was chosen after examining the sensitivity. Even though proper Lamb wave mode was selected, the tomographic image had blurred zones. Blurring reduces the precision of an ultrasonic image and makes it more difficult to distinguish the dimensions of the flaw. To enhance the tomographic image of the CLP, deep learning architecture such as U-Net was utilized for the segmentation of the experimental ultrasonic tomographic image, which includes an encoder and decoder part for better visualization of the tomographic image. Nevertheless, collecting enough ultrasonic images to train the U-Net model was not economically feasible, and only a small number of the CLP specimens can be tested. Thus, it was necessary to utilize transfer learning and get the values of the parameters from a pre-trained model with a much larger dataset as a starting point for a new task, rather than training a new model from scratch. Through these deep learning approaches, we were able to eliminate the blurred section of the ultrasonic tomography, leading to images with clear edges of defects and no blurred zones.

Magnetic resonance elastography (MRE) is a technique to identify the viscoelastic moduli of biological tissues by solving the inverse problem from the displacement field of viscoelastic wave propagation in a tissue measured by MRI. Because finite element analysis (FEA) of MRE evaluates not only the viscoelastic model for a tissue but also the efficiency of the inversion algorithm, we developed FEA for MRE using commercial software called ANSYS, the Zener model for displacement field of a wave inside tissue, and an inversion algorithm called the modified integral method. The profile of the simulated displacement field by FEA agrees well with the experimental data measured by MRE for gel phantoms. Similarly, the value of storage modulus (i.e., stiffness) recovered using the modified integral method with the simulation data is consistent with the value given in FEA. Furthermore, applying the suggested FEA to a human liver demonstrates the effectiveness of the present simulation scheme.

Since the abstract can be found at the beginning of most scientific articles and is an essential part of the article, several attempts have been made to explore the rhetorical moves of abstracts in various research fields. These studies dealt only with accepted articles since they can be easily accessed. Although the findings of such works have some pedagogical implications for academic writing courses for young researchers who are relatively new to their fields, they do not contribute enough to the transparency of the peer review processes conducted in research fields. Increasing transparency requires considering rejected articles since they help to clarify the decision criteria in the peer review. Based on 591 abstracts of accepted or rejected articles submitted to (), the present study aimed at exploring the differences between the accepted and rejected abstracts. The results show that there are significant differences in the structures of the abstracts. Since we also successfully develop a classification model for the decision using a machine-learning technique, the findings of this study have some implications for developing a semi-automatic reviewing system that can reduce the reviewer's burden and increase the review quality.

The in-cylinder flow prior to combustion is considered to be one of the most important aspects controlling the combustion process in an engine. More specifically, the large-scale structures present in the cylinder, so-called tumble and swirl, before compression are believed to play a major role into the mixing and combustion processes. Their development during the intake stroke and their final strength depend mainly (but not only) on the inlet port design. In the present study, the turbulent large-scale structures during the intake stroke are investigated in a unique water-analogue engine where inlet ports and valve timings can easily be configured and tested. The flow field in the cylinder volume is reconstructed through multi-planar stereoscopic particle image velocimetry measurements which reveal a wealth of vortical structures during the stroke's various phases. The aim of the present paper is to present and show results from a unique setup which can serve as a test bench for optimisation of inlet port designs to obtain a desired vortical pattern in the cylinder after the intake stroke is finished. This setup can simulate the intake stroke in a much more realistic way as compared to a through-flow setup with a fixed valve lift.

Many visual analytics have been developed for examining scientific publications comprising wealthy data such as authors and citations. The studies provide unprecedented insights on a variety of applications, e.g., literature review and collaboration analysis. However, visual information (e.g., figures) that is widely employed for storytelling and methods description are often neglected. We present , an interactive storyboard for exploring visual information in scientific publications. We harvest a new dataset of a large corpora of figures, using an automatic figure extraction method. Each figure contains various attributes such as dominant color and width/height ratio, together with faceted metadata of the publication including venues, authors, and keywords. To depict these information, we develop an intuitive interface consisting of three components: (1) Faceted View enables efficient query by publication metadata, benefiting from a nested table structure, (2) Storyboard View arranges paper rings-a well-designed glyph for depicting figure attributes, in a themeriver layout to reveal temporal trends, and (3) Endgame View presents a highlighted figure together with the publication metadata. We illustrate the applicability of with case studies on two datasets, i.e., 10-year IEEE VIS publications, and publications by a research team at CVPR, ICCV, and ECCV conferences. Quantitative and qualitative results from a formal user study demonstrate the efficiency of in exploring visual information in scientific publications.

We propose a user-centered visual explorer (UcVE) for progressive comparing multiple visualization units in spatiotemporal space. We create unique unit visualization with the customizable aggregated view based on the visual metaphor of flower bursts. Each visualization unit is encoded with the abstraction of spatiotemporal properties. To reduce user cognition load, UcVE allows users to visualize, save, and track in-the-process exploration results. In coordination of storage sequence and block tracking views, UcVE can facilitate comparison with multiple visualization units concurrently, selected from historical and current exploration results. UcVE offers a flexible geo-based layout, with aggregation functions and temporal views of the timeline with categorized events, to maximize the user's exploration capabilities. Finally, we demonstrate the usefulness by using COVID-19 datasets, case studies with different user scenarios, and expert feedback.

A series of experiments involving the particle image velocimetry technique are carried out to analyse the quantitative effectiveness of the synthesized vortical structures towards actual flow separation control. The streamwise vortices are synthesized from the synthetic jet actuator and introduced into the attached and separating boundary layer developed on the flat plate surface. Two types of actuators with different geometrical set-ups are used to analyse the evolution of vortical structures in the near wall region and their impact towards achieving separation delay in the boundary layer. First, a single circular jet is synthesized by varying actuator operating parameters and issued into the boundary layer to evaluate the dynamics of the interaction between the vortical structures and the near wall low momentum fluid in the separated region. Second, an array of jets has been issued into the artificially separated region to assess the effectiveness of various vortical structures towards achieving the reattachment of the separated flow in the streamwise direction.

In this study, the effect of cutting the end of a thick airfoil and adding a cavity on its flow pattern is studied experimentally using PIV technique. First, by cutting 30% chord length of the Riso airfoil, a thick blunt trialing-edge airfoil is generated. The velocity field around the original airfoil and the new airfoil is measured by PIV technique and compared with each other. Then, adding two parallel plates to the end of the new airfoil forms the desired cavity. Continuous measurement of unsteady flow velocity over the Riso airfoil with thick blunt trailing edge and base cavity is the most important innovation of this research. The results show that cutting off the end of the airfoil decreases the wake region behind the airfoil, when separation occurs. Moreover, adding a cavity to the end of the thickened airfoil causes an increase in momentum and a further decrease in the wake behind the trailing edge that leads to a drag reduction in comparison with the thickened airfoil without cavity. Furthermore, using cavity decreases the Strouhal number and vortex shedding frequency.

In this paper, an overview-based interactive visualization for temporally long dynamic data sequences is described. To reach this goal, each data object at a certain time point can be mapped to a number value based on a given property. Among others, a property is application-dependent and can be number of vertices, number of edges, average degree, density, number of self-loops, degree (maximum and total), or edge weight (minimum, maximum, and total) for dynamic graph data, but it can as well be the number of ball contacts in a football match, or the time-dependent visual attention paid to a stimulus in an eye tracking study. To achieve an overview over time, an aggregation strategy based on either the mean, minimum, or maximum of two values is applied. This temporal value aggregation generates a triangular shape with an overview of the entire data sequence as the peak. The color coding can be adjusted, forming visual patterns that can be rapidly explored for certain data features over time, supporting comparison tasks between the properties. The usefulness of the approach is illustrated by means of applying it to dynamic graphs generated from US domestic flight data as well as to dynamic Covid-19 infections on country levels.

Recent advances in digital signage technology have improved the ability to visually select specific items within a group. Although this is due to the ability to dynamically update the display of items, the corresponding layout schemes remain a subject of research. This paper explores the sophisticated layout of items by respecting the underlying context of searching for favorite items. Our study begins by formulating the static placement of items as an optimization problem that incorporates aesthetic layout criteria as constraints. This is further extended to accommodate the dynamic placement of items for more proactive visual exploration based on the ongoing search context. Our animated layout is driven by analyzing the distribution of eye gaze through an eye-tracking device, by which we infer how the most attractive items lead to the finally wanted ones. We create a planar layout of items as a context map to establish association rules to dynamically replace existing items with new ones. For this purpose, we extract the set of important topics from a set of annotated texts associated with the items using matrix factorization. We also conduct user studies to evaluate the validity of the design criteria incorporated into both static and dynamic placement of items. After discussing the pros and cons of the proposed approach and possible themes for future research, we conclude this paper.

Peer-to-peer accommodation is developing rapidly in the era of sharing economy, and the visual recommendation of accommodation is also an urgent problem to be solved. Meanwhile, user-generated content is critical in P2P accommodations, because they contain a wealth of information about the opinions and experiences of users, which helps understand consumer decisions and improve products and services better. However, the huge volume of reviews makes it difficult for potential customers to gain useful insights and for managers to track customer opinions. In this paper, we propose a complete pipeline for recommending personalized accommodations for consumers, while also providing insights for managers. First, we use topic modeling techniques to mining opinions from review. Second, we build a deep learning network for review sentiment analysis. Third, we perform sentiment analysis of the reviews at the aspect level to obtain the sentiment vector representation of the accommodation. Finally, we propose a personalized accommodation recommendation method based on the above work. Moreover, we design a visual analytic system with a user-friendly interface to facilitate interactive analysis. Evaluation including user and case studies demonstrates the usefulness and effectiveness of our method and system.

The recent development in the data analytics field provides a boost in production for modern industries. Small-sized factories intend to take full advantage of the data collected by sensors used in their machinery. The ultimate goal is to minimize cost and maximize quality, resulting in an increase in profit. In collaboration with domain experts, we implemented a data visualization tool to enable decision-makers in a plastic factory to improve their production process. The tool is an interactive dashboard with multiple coordinated views supporting the exploration from both local and global perspectives. In summary, we investigate three different aspects: methods for preprocessing multivariate time series data, clustering approaches for the already refined data, and visualization techniques that aid domain experts in gaining insights into the different stages of the production process. Here we present our ongoing results grounded in a human-centered development process. We adopt a formative evaluation approach to continuously upgrade our dashboard design that eventually meets partners' requirements and follows the best practices within the field. We also conducted a case study with a domain expert to validate the potential application of the tool in the real-life context. Finally, we assessed the usability and usefulness of the tool with a two-layer summative evaluation that showed encouraging results.

Continuous research and development of novel tourism routes is necessary for tourism service providers to improve the tourist experience and industrial competitiveness. However, the route planning is cumbersome due to the time-consuming, extensive, and costly field study. Most of the existing route planning studies focus on recommending tourism routes for users based on attraction characteristics or tourist behavior features, which are generally unexplainable due to the black-box approaches they use. Other solutions allow users to customize itineraries through an interactive interface but often lack guidance from the aspect of route evaluation or destination image perception. In this paper, we thoroughly discuss the requirements and design tasks with domain experts and propose TriPlan, an interactive visual analytics system that provides intuitive planning guidance for tourism product developers. We design and improve multiple coordinated visualizations to facilitate analysis from the perspectives of overall route pattern and individual destination image. We also develop a hierarchical planning view to display the structural information of a plan. In addition, we introduce an automatic route optimization algorithm and multiple interactions to assist users in optimizing and adjusting the itineraries. Finally, we evaluate the usability and effectiveness of our system through three case studies and quantitative and qualitative interviews with the domain experts on real-world datasets.

The recent Coronavirus Disease 2019 pandemic has highlighted the importance of indoor ventilation. In particular, ventilation is crucial in residential spaces and workspaces, where people spent most of their day. Natural ventilation is a cost-effective method for improving indoor ventilation. It can provide safe and comfortable residential and working environments without additional energy consumption. In this study, the ventilation performance was experimentally studied by measuring the concentration of ultrafine particulate matter according to the opening conditions of the windows and door of an office model in a wind tunnel. Furthermore, the internal flow structure in the office model was quantitatively analyzed through particle image velocimetry to determine the factors that affected the ventilation performance. The mean velocity inside the model and the ventilation performance increased with the opening angle of the windows. In particular, the opening condition of the door strongly affected the ventilation performance. This study is expected to provide a guideline for effectively improving the ventilation performance in indoor spaces.

During the ground operation of aircraft, the interaction between the propulsor-induced flow field and the ground may lead to the generation of ground vortices. Utilizing numerical approaches, the source of vorticity entering ground vortices is investigated. The results show that the production of wall-parallel components of vorticity has a strong contribution from the wall-parallel components of the pressure gradient on the wall, which is generated by the action of the propulsor. This mechanism is a supplementation for the vorticity transported from the far-field boundary layer, which has been assumed the main vorticity source in a number of previous publications. Furthermore, the quantitative prediction of the occurrence of ground vortices is performed from the numerical results. As the distance of the propeller form the ground decreases, and as the thrust of the propeller increases, ground vortices are generated from the ground and enter the propeller. In addition, the vortices which exist near the ground but does not enter the propeller plane are observed and visualized by three-dimensional data.

The interaction between a propeller and its self-induced vortices originating on the ground is investigated in a scaled experiment. The velocity distribution in the flow field in two different planes containing the self-induced vortices is measured by particle image velocimetry (PIV). These planes are a wall-parallel plane in close proximity to the ground and a wall-normal plane just upstream of the propeller. Based on the visualization of the flow field in these two planes, the occurrence of ground vortices and its domain boundary are analysed. The elevation of the propeller from the ground and the thrust of the propeller are two parameters that determine the occurrence of ground vortices. The main features of the propeller inflow in the presence of the ground vortices are highlighted. Moreover, the analysis of the non-uniform inflow in the azimuthal direction shows that with increasing the propeller thrust coefficient and decreasing the elevation of the propeller above the ground, the variation of the inflow angle of the blade increases.

The high-resolution scanning devices developed in recent decades provide biomedical volume datasets that support the study of molecular structure and drug design. Isosurface analysis is an important tool in these studies, and the key is to construct suitable description vectors to support subsequent tasks, such as classification and retrieval. Traditional methods based on handcrafted features are insufficient for dealing with complex structures, while deep learning-based approaches have high memory and computation costs when dealing directly with volume data. To address these problems, we propose IsoExplorer, an isosurface-driven framework for 3D shape analysis of biomedical volume data. We first extract isosurfaces from volume data and split them into individual 3D shapes according to their connectivity. Then, we utilize octree-based convolution to design a variational autoencoder model that learns the latent representations of the shape. Finally, these latent representations are used for low-dimensional isosurface representation and shape retrieval. We demonstrate the effectiveness and usefulness of IsoExplorer via isosurface similarity analysis, shape retrieval of real-world data, and comparison with existing methods.

A novel method, called a posteriori "VFM accuracy estimation" (VAE), for resolving an intrinsic VFM problem is proposed. The problem is that VFM uncertainty can easily vary according to blood flows through an echocardiographic imaged plane (i.e., "through-plane" flows), and it is unknown. Knowing the VFM uncertainty for each patient will make it possible to refine the quality of VFM-based diagnosis. In the present study, VAE was derived on the basis of an error-propagation analysis and a statistical analysis. The accuracy of VAE with a pulsatile left-ventricle phantom was experimentally investigated for realistic cases with through-plane flows. VAE was validated by comparing VFM uncertainty (S.D.) estimated by VAE with VFM uncertainty measured by particle-image velocimetry (PIV) for different imaged planes. VAE accurately estimated the S.D. of VFM uncertainty measured by PIV for all cases with different image planes ( > 0.6 and  < 0.001). These findings on VFM accuracy will provide the basis for widespread clinical application of VFM-based diagnosis.

Exploring large graphs is difficult due to their large size and semantic information such as node attributes. Extracting only a subgraph relevant to the user-specified nodes (called focus nodes) is an effective strategy for exploring a large graph. However, existing approaches following this strategy mainly focus on graph topology and do not fully consider node attributes, resulting in the lack of clear semantics in the extracted subgraphs. In this paper, we propose a novel approach called TS-Extractor that can extract a relevant subgraph around the user-selected focus nodes to help the user explore the large graph from a local perspective. By combining the graph topology and the user-selected node attributes, TS-Extractor can extract and visualize a connected subgraph that contains as many nodes sharing the same/similar attribute values with the focus nodes as possible, thereby providing the user with clear semantics. Based on TS-Extractor, we develop a Web-based graph exploration system that allows users to interactively extract, analyze and expand subgraphs. Through two case studies and a user study, we demonstrate the usability and effectiveness of TS-Extractor.

An experimental study has been conducted to visualise the instantaneous streamwise and spanwise flow patterns of a normal shock wave impinging over a rounded contour bump in a Mach 1.3 free-stream. A quartz-made transparent shock generator was used, so that instantaneous images could be captured during the oil-flow visualisation experiments. Fluorescent oil with three different colours was used in the surface oil-flow visualisation experiment to enhance the visualisation of flow mixing and complicated flow features that present in the flow field. Experimental data showed that the rounded contour bump could split the impinging normal shock wave into a or a series of lambda-shaped shock wave structure(s). In addition, it was found that the flow pattern and the shock wave structures that appeared over the rounded contour bump depended highly on the impinging location of the normal shock wave. The flow pattern shown in this study agreed with the findings documented in literature. Moreover, it was observed from the instantaneous oil streaks that the normal shock impinging location also affected the size and the formation location of the spanwise counter-rotating vortices downstream of the bump crest. Finally, it was concluded that the terminating shock could distort the oil streaks that left over the surface of the contour bump. Therefore, the use of the transparent normal shock wave generator is recommended when conducting experiments with normal shock wave impingement involved.

Hashtag data are common and easy to acquire. Thus, they are widely used in studies and visual data storytelling. For example, a recent story by China Central Television Europe depicts Brexit as a hashtag movement displayed on an animated bubble chart. However, creating such a story is usually laborious and tedious, because narrators have to switch between different tools and discuss with different collaborators. To reduce the burden, we develop a prototype system to help explore the bubbles' movement by automatically inserting animations connected to the storytelling of the video creators and the interaction of viewers to those videos. We demonstrate the usability of our method through both use cases and a semi-structured user study.